/*=========================================================================
 *
 *  Copyright NumFOCUS
 *
 *  Licensed under the Apache License, Version 2.0 (the "License");
 *  you may not use this file except in compliance with the License.
 *  You may obtain a copy of the License at
 *
 *         https://www.apache.org/licenses/LICENSE-2.0.txt
 *
 *  Unless required by applicable law or agreed to in writing, software
 *  distributed under the License is distributed on an "AS IS" BASIS,
 *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *  See the License for the specific language governing permissions and
 *  limitations under the License.
 *
 *=========================================================================*/
#ifndef itkMiniPipelineSeparableImageFilter_hxx
#define itkMiniPipelineSeparableImageFilter_hxx

#include "itkProgressAccumulator.h"

/*
 *
 * This code was contributed in the Insight Journal paper:
 * "Efficient implementation of kernel filtering"
 * by Beare R., Lehmann G
 * https://hdl.handle.net/1926/555
 * https://www.insight-journal.org/browse/publication/160
 *
 */

namespace itk
{
template <typename TInputImage, typename TOutputImage, typename TFilter>
MiniPipelineSeparableImageFilter<TInputImage, TOutputImage, TFilter>::MiniPipelineSeparableImageFilter()
{
  // create the pipeline
  for (unsigned int i = 0; i < ImageDimension; ++i)
  {
    m_Filters[i] = FilterType::New();
    m_Filters[i]->ReleaseDataFlagOn();
    if (i > 0)
    {
      m_Filters[i]->SetInput(m_Filters[i - 1]->GetOutput());
    }
  }

  m_Cast = CastType::New();
  m_Cast->SetInput(m_Filters[ImageDimension - 1]->GetOutput());
  m_Cast->SetInPlace(true);
}

template <typename TInputImage, typename TOutputImage, typename TFilter>
void
MiniPipelineSeparableImageFilter<TInputImage, TOutputImage, TFilter>::Modified() const
{
  Superclass::Modified();
  for (unsigned int i = 0; i < ImageDimension; ++i)
  {
    m_Filters[i]->Modified();
  }
  m_Cast->Modified();
}

template <typename TInputImage, typename TOutputImage, typename TFilter>
void
MiniPipelineSeparableImageFilter<TInputImage, TOutputImage, TFilter>::SetNumberOfWorkUnits(ThreadIdType nb)
{
  Superclass::SetNumberOfWorkUnits(nb);
  for (unsigned int i = 0; i < ImageDimension; ++i)
  {
    m_Filters[i]->SetNumberOfWorkUnits(nb);
  }
  m_Cast->SetNumberOfWorkUnits(nb);
}

template <typename TInputImage, typename TOutputImage, typename TFilter>
void
MiniPipelineSeparableImageFilter<TInputImage, TOutputImage, TFilter>::SetRadius(const RadiusType & radius)
{
  Superclass::SetRadius(radius);

  // set up the kernels
  for (unsigned int i = 0; i < ImageDimension; ++i)
  {
    RadiusType rad;
    rad.Fill(0);
    rad[i] = radius[i];
    m_Filters[i]->SetRadius(rad);
  }
}

template <typename TInputImage, typename TOutputImage, typename TFilter>
void
MiniPipelineSeparableImageFilter<TInputImage, TOutputImage, TFilter>::GenerateData()
{
  this->AllocateOutputs();

  // set up the pipeline
  m_Filters[0]->SetInput(this->GetInput());

  // Create a process accumulator for tracking the progress of this minipipeline
  auto progress = ProgressAccumulator::New();
  progress->SetMiniPipelineFilter(this);
  for (unsigned int i = 0; i < ImageDimension; ++i)
  {
    progress->RegisterInternalFilter(m_Filters[i], 1.0 / ImageDimension);
  }

  m_Cast->GraftOutput(this->GetOutput());
  m_Cast->Update();
  this->GraftOutput(m_Cast->GetOutput());
}
} // namespace itk

#endif
